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Zero variance in Markov chain Monte Carlo with an application to credit risk estimation

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  • Tenconi Paolo

    (Department of Economics, University of Insubria, Italy)

Abstract

We propose a general purpose variance reduction technique for Markov Chain Monte Carlo estimators based on the Zero-Variance principle introduced in the physics literature by Assaraf and Ca arel ( 1999). The potential of the new idea is illustrated with some toy examples and a real application to Bayesian inference for credit risk estimation.

Suggested Citation

  • Tenconi Paolo, 2008. "Zero variance in Markov chain Monte Carlo with an application to credit risk estimation," Economics and Quantitative Methods qf0804, Department of Economics, University of Insubria.
    • Handle: RePEc:ins:quaeco:qf0804
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    File URL: https://www.eco.uninsubria.it/RePEc/pdf/QF2008_04.pdf
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    References listed on IDEAS

    as
    1. M. Durea, 2007. "First and Second-Order Lagrange Claims for Set-Valued Maps," Journal of Optimization Theory and Applications, Springer, vol. 133(1), pages 111-116, April.
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